Comments (3)
romainVala
commented on July 22, 2024
2
Hi
this is the case with subject's history
If you use a torchio Subject, then after any transform, you can get the specific parameters used, by looking at subject.history
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fepegar
commented on July 22, 2024
1
@LvanderGoten here are some self-supervised papers using TorchIO, in case they help: https://scholar.google.com/scholar?hl=en&as_sdt=2005&sciodt=0%2C5&cites=11818021599290863762&scipsc=1&q=self-supervised&btnG=
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LvanderGoten
commented on July 22, 2024
Thank you so much!
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Related Issues (20)
- Suggestions the modifying default value of prefetch_factor and the argument to set it for minimize the blocking-bottleneck between fetch subject and generate patch in Queue HOT 1
- Different transforms applied to CT and label HOT 11
- The Affine matrix does not change after applying the augmentations HOT 3
- Custom loader not used when loading data lazily HOT 2
- Seed is not working HOT 2
- Silenced exception makes it harder to debug custom Transforms HOT 5
- Resample
- tio.Resample does not work with custom image class HOT 2
- Setting NUM_SAMPLES when using sampler with Queue HOT 3
- RescaleIntensity - multiple calls HOT 9
- Halve queue length when using DDP HOT 2
- bug in rotation part of tio.transforms.RandomAffine HOT 4
- get_subjects_from_batch has a hick-up with int metadata HOT 5
- masking_method in Mask class is not saved as argument (preventing applying the inverse transform)
- RandomAffine raises an error when isotropic=True and 3 elements are given for scales HOT 10
- Queue is not respecting the batch size HOT 1
- Resample an image by providing only the target affine HOT 1
- Supporting PyTorch 2.3 HOT 11
- Cannot copy subclass of Subject with keyword arguments HOT 1
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